Statistical variability of top of atmosphere cloud-free shortwave aerosol radiative effect
Titel:
Statistical variability of top of atmosphere cloud-free shortwave aerosol radiative effect
Auteur:
T. A. Jones S. A. Christopher
Verschenen in:
Atmospheric chemistry and physics
Paginering:
Jaargang 7 (2007) nr. 11 pagina's 2937-2948
Jaar:
2007
Inhoud:
The statistical variability of globally averaged MODIS aerosol optical thickness at 0.55 μm (AOT) and top of atmosphere CERES cloud-free shortwave radiative effect (SWRE) is presented. Statistical variability is defined as the robustness of globally averaged statistics relative to data distribution. At the CERES footprint level, which we label "raw data", both the AOT and SWRE data derived from clear-sky CERES-SSF products show significant deviations from a normal distribution as evidenced by high skewness values. The spatial and temporal distribution of the data is also not uniform, with a greater concentration of data occurring in aerosol heavy-regions. As a result, globally averaged AOT and SWRE are overestimated when derived from raw data alone. To compensate, raw data are gridded into 2×2 degree grid-cells (called "gridded" data) to reduce the effect of spatial non-uniformity. However, the underlying non-normal distribution remains and manifests itself by increasing the uncertainty of grid-cell values. Globally averaged AOT and SWRE derived from a gridded dataset are substantially lower than those derived from raw data alone. The range of globally averaged AOT and SWRE values suggests that up to a 50% statistical variability exists, much of which is directly tied to how the data are manipulated prior to averaging. This variability increases when analyzing aerosol components (e.g. anthropogenic) since component AOT (and SWRE) may not exist at all locations were AOT is present. As a result, regions where a particular component AOT does not exist must either not be included in the global average or have data within these regions set to null values. However, each method produces significantly different results. The results of this work indicate simple mean and standard deviation statistics do not adequately describe global aerosol climate forcing data sets like the one used here. We demonstrate that placing raw observations on to a uniform grid is a necessary step before calculating global statistics. However, this by no means eliminates uncertainty in globally averaged AOT and SWRE values, while adding its own set of assumptions. When reporting any globally averaged statistic, it is important to report corresponding distribution and coverage information, in the form of skewness values, probability density functions, and spatial distribution plots, to help quantify its usefulness and robustness.
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